Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

Authors

  • Amir Mahmud Husein Universitas Prima Indonesia, Indonesia
  • Februari Kurnia waruwu Universitas Prima Indonesia, Indonesia
  • Yacobus M.T. Batu Bara Universitas Prima Indonesia, Indonesia
  • Meleyaki Donpril Universitas Prima Indonesia, Indonesia
  • Mawaddah Harahap Universitas Prima Indonesia

DOI:

10.33395/sinkron.v6i1.11191

Abstract

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

GS Cited Analysis

Downloads

Download data is not yet available.

References

J. Wu et al., “An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm,” Math. Probl. Eng., vol. 2020, no. November 2017, 2020, doi: 10.1155/2020/8884227.

S. G. Carbajal, “Customer segmentation through path reconstruction,” Sensors, vol. 21, no. 6, pp. 1–17, Mar. 2021, doi: 10.3390/s21062007.

Dedi, M. I. Dzulhaq, K. W. Sari, S. Ramdhan, R. Tullah, and Sutarman, “Customer Segmentation Based on RFM Value Using K-Means Algorithm,” Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, 2019, doi: 10.1109/ICIC47613.2019.8985726.

O. Piskunova and R. Klochko, “Classification of e-commerce customers based on Data Science techniques,” CEUR Workshop Proc., vol. 2649, pp. 6–20, 2020.

E. Lee, J. Kim, and D. Jang, “Load profile segmentation for effective residential demand response program: Method and evidence from Korean pilot study,” Energies, vol. 16, no. 3, p. 1348, Mar. 2020, doi: 10.3390/en13061348.

M. P. Fernandes, J. L. Viegas, S. M. Vieira, and J. M. C. Sousa, “Segmentation of residential gas consumers using clustering analysis,” Energies, vol. 10, no. 12, p. 2047, Dec. 2017, doi: 10.3390/en10122047.

T.-Y. Ou and Y. J. Chen, “Optimal Segmentation over a Generalized Customer Distribution,” Axioms, vol. 10, no. 2, p. 98, May 2021, doi: 10.3390/axioms10020098.

D. Kamthania, A. Pahwa, and S. S. Madhavan, “Market segmentation analysis and visualization using K-mode clustering algorithm for E-commerce business,” J. Comput. Inf. Technol., vol. 26, no. 1, pp. 57–68, 2018, doi: 10.20532/cit.2018.1003863.

K. Baek, S. Kim, E. Lee, Y. Cho, and J. Kim, “Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector,” Energies, vol. 14, no. 4, p. 866, Feb. 2021, doi: 10.3390/

Downloads


Crossmark Updates

How to Cite

Husein , A. M. ., waruwu , F. K. ., Batu Bara , Y. M. ., Donpril, M. ., & Harahap, M. (2021). Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(2B), 137-143. https://doi.org/10.33395/sinkron.v6i1.11191

Most read articles by the same author(s)

1 2 3 > >>